OSCR

Linking brain structure to stress reactivity: cingulate surface area predicts acute cortisol responses.

A correction to this paper has been published: the notice, 42047098, from Europe PMC.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods and materials › Data analysis ↔ 01_palm/code/03_plot_results.ipynb, lines 124–214 · score 0.91 · rostral anterior cingulate, caudal anterior cingulate, posterior cingulate, Cortical thickness, parahippocampus, precuneus
  2. [2] § Methods and materials › Data analysis ↔ 03_stress-ml/code/01_predict.py, lines 252–317 · score 0.85 · nested cross validation, outer folds, model evaluation, SHAP, inner, optimization
  3. [3] § Results › Permutation analysis of linear models (PALM) results ↔ 01_palm/code/03_plot_results.ipynb, lines 124–214 · score 0.71 · rostral anterior cingulate, caudal anterior cingulate, lateral orbitofrontal, posterior cingulate, insula, PALM
  4. [4] § Methods and materials › Data analysis ↔ 02_whole-brain/code/04_run_cluster-sim.sh, the whole file · a weak match · score 0.64 · mri_glmfit, FreeSurfer, Simulation, spaces, cluster, thickness
  5. [5] § Methods and materials › Data analysis ↔ 01_palm/code/00_prepare_files.ipynb, lines 30–85 · score 0.59 · parahippocampus, precuneus, accumbens, putamen, thalamus, rostral
  6. [6] § Methods and materials › Data analysis ↔ 02_whole-brain/code/03_run-glms.sh, the whole file · a weak match · score 0.58 · mri_glmfit, FreeSurfer, fsaverage, cortex, thickness, volume

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 214 lines · 5.8 KB · GPL-3.0 · 2 matches

  1. # %%
  2. import os
  3. import os.path as op
  5. import matplotlib.pyplot as plt
  6. import pandas as pd
  7. import seaborn as sns
  8. from scipy import stats
  9. from sklearn.linear_model import LinearRegression
  10. from sklearn.preprocessing import OneHotEncoder
  13. # %%
  14. WORKING_PATH = "..." # Set your working path here.
  16. beh = pd.read_csv(
  17. op.join(WORKING_PATH, "data/beh/beh_residualized_extended_050225.csv")
  18. )
  19. aparc = pd.read_csv(
  20. op.join(WORKING_PATH, "data/fs-measures/aparcstats2table_combined.csv")
  21. )
  22. aseg = pd.read_csv(
  23. op.join(WORKING_PATH, "data/fs-measures/aseg_stats_combined.csv")
  24. ).rename(columns={"Measure:volume": "id"})
  26. beh = beh[beh["site"] == "regensburg"]
  27. db = pd.merge(beh, aparc, on="id", how="inner").merge(aseg, on="id", how="inner")
  28. db
  30. # %%
  31. def plot_brain_correlations(data, x, y, y_label, out_path=None):
  32. # Set style and color palette
  33. sns.set_style(
  34. "whitegrid",
  35. {"axes.grid": False, "xtick.bottom": False, "ytick.left": False},
  36. )
  37. palette = {"male": "#2E86AB", "female": "#F24236"}
  39. # Create figure
  40. plt.figure(figsize=(12, 11), dpi=300)
  42. # Create jointplot with enhanced styling
  43. g = sns.jointplot(
  44. data=data,
  45. x=x,
  46. y=y,
  47. hue="sex",
  48. kind="scatter",
  49. height=10,
  50. ratio=8,
  51. palette=palette,
  52. marginal_kws=dict(common_norm=False, fill=True),
  53. joint_kws=dict(alpha=0.7, s=100),
  54. )
  56. # Add regression lines with confidence intervals
  57. for sex_group in data["sex"].unique():
  58. subset = data[data["sex"] == sex_group]
  59. sns.regplot(
  60. data=subset,
  61. x=x,
  62. y=y,
  63. scatter=False,
  64. line_kws={"linestyle": "--", "linewidth": 2},
  65. ci=95,
  66. ax=g.ax_joint,
  67. color=palette[sex_group],
  68. )
  70. # Add overall regression line
  71. sns.regplot(
  72. data=data,
  73. x=x,
  74. y=y,
  75. scatter=False,
  76. color="black",
  77. line_kws={"linewidth": 2},
  78. ci=95,
  79. ax=g.ax_joint,
  80. )
  82. # Customize labels and appearance
  83. g.ax_joint.set_xlabel(
  84. "Cortisol increase (nmol/l)".title(), fontsize=20, fontweight="bold"
  85. ).set_visible(False) ## To set the visibility off.
  86. g.ax_joint.set_ylabel(y_label, fontsize=20, fontweight="bold").set_visible(
  87. False
  88. ) ## To set the visibility off.
  89. g.ax_joint.tick_params(labelsize=18)
  90. # Remove axis values
  91. # g.ax_joint.set_xticklabels([])
  92. # g.ax_joint.set_yticklabels([])
  94. # Customize legend
  95. g.ax_joint.legend(
  96. title="Sex",
  97. title_fontsize=12,
  98. fontsize=11,
  99. bbox_to_anchor=(0.95, 0.15),
  100. frameon=True,
  101. edgecolor="black",
  102. ).set_visible(False)
  104. # Make axes thicker
  105. g.ax_joint.spines["bottom"].set_linewidth(2)
  106. g.ax_joint.spines["left"].set_linewidth(2)
  107. g.ax_joint.tick_params(width=2)
  109. # Adjust layout and save
  110. plt.tight_layout()
  111. if out_path:
  112. plt.savefig(out_path, dpi=500, bbox_inches="tight")
  113. plt.close()
  116. def correct_for_site(data, area, site_col="site"):
  117. # Correct for site effects using linear regression
  118. site = OneHotEncoder(sparse_output=False, drop="first").fit_transform(
  119. data[[site_col]]
  120. )
  121. data[area] = data[area] - LinearRegression().fit(site, data[area]).predict(site)
  122. return data
  124. # %%
  125. # Prepare Brain Imaging Data for PALM
  126. corr_plot_path = op.join(WORKING_PATH, "palm_regensburg_cycle/plots")
  127. os.makedirs(corr_plot_path, exist_ok=True)
  129. cortical_areas = {
  130. "rostralanteriorcingulate": "Rostral Anterior Cingulate",
  131. "caudalanteriorcingulate": "Caudal Anterior Cingulate",
  132. "posteriorcingulate": "Posterior Cingulate",
  133. "parahippocampal": "Parahippocampal Gyrus",
  134. "lateralorbitofrontal": "Lateral Orbitofrontal Gyrus",
  135. "medialorbitofrontal": "Medial Orbitofrontal Gyrus",
  136. "insula": "Insula",
  137. "precuneus": "Precuneus",
  138. }
  140. subcortical_areas = {
  141. "Thalamus-Proper": "Thalamus",
  142. "Caudate": "Caudate Nucleus",
  143. "Accumbens-area": "Nucleus Accumbens",
  144. "Putamen": "Putamen",
  145. "Hippocampus": "Hippocampus",
  146. "Amygdala": "Amygdala",
  147. }
  149. # Cortical Thickness
  150. for hemi in ["lh", "rh"]:
  151. for area, area_name in cortical_areas.items():
  152. data = db[
  153. [
  154. "site",
  155. "sex",
  156. "increase",
  157. f"{hemi}_{area}_thickness",
  158. ]
  159. ]
  160. out_file = f"{corr_plot_path}/{hemi}_{area}_thickness_increase.png"
  161. if op.exists(out_file):
  162. continue
  163. plot_brain_correlations(
  164. data,
  165. "increase",
  166. f"{hemi}_{area}_thickness",
  167. f"{area_name} thickness (mm)",
  168. out_file,
  169. )
  171. # Surface Area
  172. for hemi in ["lh", "rh"]:
  173. for area, area_name in cortical_areas.items():
  174. data = db[
  175. [
  176. "site",
  177. "sex",
  178. "increase",
  179. f"{hemi}_{area}_area",
  180. ]
  181. ]
  182. out_file = f"{corr_plot_path}/{hemi}_{area}_area_increase.png"
  183. if op.exists(out_file):
  184. continue
  185. plot_brain_correlations(
  186. data,
  187. "increase",
  188. f"{hemi}_{area}_area",
  189. f"{area_name} surface area (mm²)",
  190. out_file,
  191. )
  193. # Subcortical Volume
  194. for hemi in ["Left", "Right"]:
  195. for area, area_name in subcortical_areas.items():
  196. area_ = f"{hemi}-{area}"
  197. data = db[
  198. [
  199. "site",
  200. "sex",
  201. "increase",
  202. area_,
  203. ]
  204. ]
  205. out_file = f"{corr_plot_path}/{hemi}_{area}_volume_increase.png"
  206. if op.exists(out_file):
  207. continue
  208. plot_brain_correlations(
  209. data,
  210. "increase",
  211. area_,
  212. f"{area_name} volume (mm³)",
  213. out_file,
  214. )

03_plot_results.ipynb at commit acc8c93, under GPL-3.0 · at the source

Overview

Authors: Emin Serin1,2, Lea Sophie Schill1, Christoph Bärtl1,2, Marina Giglberger3, Julian Konzok3,4, Hannah L. Peter3, Nina Speicher3, Ludwig Kreuzpointner3, Brigitte M. Kudielka3, Stefan Wüst3, Henrik Walter1,2, Gina-Isabelle Henze1,2
ORCID iDs: Emin Serin, Lea Sophie Schill, Christoph Bärtl, Marina Giglberger, Julian Konzok, Nina Speicher, Ludwig Kreuzpointner, Brigitte M. Kudielka, Stefan Wüst, Henrik Walter, Gina-Isabelle Henze
  1. Department of Psychiatry and Neurosciences CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
  2. German Center for Mental Health (DZPG), Partner Site Berlin-Potsdam, Berlin, Germany
  3. Institute of Psychology, University of Regensburg, Regensburg, Germany
  4. Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
Institutions: Humboldt-Universität zu Berlin (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); Charité - Universitätsmedizin Berlin (Germany); University of Regensburg (Germany)
Journal: Psychological medicine, volume 56, article e96
Dates: received 14 May 2025; accepted 26 February 2026; published online 7 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1017/s0033291726104103 · PMID 41943954 · PMCID PMC13079226 · OpenAlex W4414183550
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.12.675570
Status: code verified
Categories: human (organism), developmental (subfield)
Methods: Statistics, Machine learning, fMRI & imaging, Preprocessing
Keywords: FreeSurfer, hypothalamic–pituitary–adrenal (HPA) axis, machine learning, permutation analysis of linear models (PALM), psychosocial stress, ScanSTRESS
MeSH: Gyrus Cinguli*, Hydrocortisone*, Hypothalamo-Hypophyseal System*, Stress, Psychological*, Adolescent, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Pituitary-Adrenal System, Young Adult (* major topic)
Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (HE9212/1-1 (Project Number 513531314), FOR5187 (Project Number 442075332))
Citations: cited by 2 papers (Europe PMC); 107 references in the paper
Notices: A correction to this paper has been published (42047098, from Europe PMC)

Abstract

Background: Altered stress responses are closely linked to mental disorders, but the role of brain structure in acute cortisol responses to psychosocial stress remains underexplored, particularly in healthy individuals. Previous studies, with predominantly small samples, primarily focused on selected limbic regions and functional measures. Thus, this study investigates associations between brain structure and cortisol responses to psychosocial stress, exploring if hypothalamic–pituitary–adrenal axis reactivity can be predicted from brain morphology.

Methods: Our study included 291 subjects (157 females, 18–62 years) and consisted of two parts. First, a confirmatory analysis examined associations between specific cortical surface area, thickness, and subcortical volume with stress-induced cortisol increases using Permutation Analysis of Linear Models (PALM). Second, we conducted an exploratory whole-brain vertex-wise analysis, followed by out-of-sample prediction of cortisol increases from structural measures.

Results: We found consistent negative associations between cingulate cortex (CC) sub-structures and acute cortisol increases. In PALM- and whole-brain analysis, a smaller surface area of the left rostral and caudal anterior cingulate cortex (cACC), posterior cingulate cortex, and right cACC were associated with higher cortisol stress responses, particularly in males. The left cACC surface area emerged as the most promising predictor in machine learning analyses. Additionally, other fronto-limbic structures were also associated with or predictive of acute cortisol reactivity.

Conclusions: Our findings demonstrate that cortical and subcortical structural measures, particularly smaller surface areas of the CC, predict acute hormonal stress responses. Notably, the left cACC emerged as the most consistent predictor, emphasizing its important role in stress reactivity.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

eminSerin/stress-fs-paper

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: acc8c93412d132e3e3c783d1030c95960009fa2b, 22 January 2026
Languages: Python (11), Jupyter (5), Shell (3), MATLAB (1)
Size: 23 files, 20 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, license file, 5 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (12 files), NumPy (7 files), scikit-learn (6 files), FreeSurfer (4 files), Matplotlib (4 files), seaborn (3 files), NiBabel (2 files), Nilearn (2 files), SHAP (2 files), SciPy (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
22 files
At the source: github.com/eminSerin/stress-fs-paper

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 20 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Analysis codes can be found at https://github.com/eminSerin/stress-fs-paper. A repository of studies that have already used and published these data is available here: https://osf.io/echja/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 12 authors, 6 keywords, 13 MeSH terms, 1 funder, 107 references, 1 integrity notice.

Cite

This paper

Serin, E., Schill, L. S., Bärtl, C., Giglberger, M., Konzok, J., Peter, H. L., Speicher, N., Kreuzpointner, L., Kudielka, B. M., Wüst, S., Walter, H., & Henze, G.-I. (2026). Linking brain structure to stress reactivity: cingulate surface area predicts acute cortisol responses. Psychological medicine, 56, e96. https://doi.org/10.1017/s0033291726104103

BibTeX

@article{serin2026linking,
author = {Serin, Emin and Schill, Lea Sophie and Bärtl, Christoph and Giglberger, Marina and Konzok, Julian and Peter, Hannah L. and Speicher, Nina and Kreuzpointner, Ludwig and Kudielka, Brigitte M. and Wüst, Stefan and Walter, Henrik and Henze, Gina-Isabelle},
title = {{Linking brain structure to stress reactivity: cingulate surface area predicts acute cortisol responses}},
journal = {Psychological medicine},
year = {2026},
month = apr,
volume = {56},
pages = {e96},
publisher = {Cambridge University Press},
issn = {0033-2917},
doi = {10.1017/s0033291726104103},
url = {https://doi.org/10.1017/s0033291726104103},
pmid = {41943954},
pmcid = {PMC13079226}
}

RIS

TY - JOUR
AU - Serin, Emin
AU - Schill, Lea Sophie
AU - Bärtl, Christoph
AU - Giglberger, Marina
AU - Konzok, Julian
AU - Peter, Hannah L.
AU - Speicher, Nina
AU - Kreuzpointner, Ludwig
AU - Kudielka, Brigitte M.
AU - Wüst, Stefan
AU - Walter, Henrik
AU - Henze, Gina-Isabelle
TI - Linking brain structure to stress reactivity: cingulate surface area predicts acute cortisol responses
T2 - Psychological medicine
J2 - Psychol Med
PY - 2026
DA - 2026/04/07
VL - 56
SP - e96
SN - 0033-2917
PB - Cambridge University Press
DO - 10.1017/s0033291726104103
UR - https://doi.org/10.1017/s0033291726104103
LA - en
ER -

CSL-JSON

{
"id": "10.1017/s0033291726104103",
"type": "article-journal",
"title": "Linking brain structure to stress reactivity: cingulate surface area predicts acute cortisol responses",
"container-title": "Psychological medicine",
"author": [
{
"family": "Serin",
"given": "Emin"
},
{
"family": "Schill",
"given": "Lea Sophie"
},
{
"family": "Bärtl",
"given": "Christoph"
},
{
"family": "Giglberger",
"given": "Marina"
},
{
"family": "Konzok",
"given": "Julian"
},
{
"family": "Peter",
"given": "Hannah L."
},
{
"family": "Speicher",
"given": "Nina"
},
{
"family": "Kreuzpointner",
"given": "Ludwig"
},
{
"family": "Kudielka",
"given": "Brigitte M."
},
{
"family": "Wüst",
"given": "Stefan"
},
{
"family": "Walter",
"given": "Henrik"
},
{
"family": "Henze",
"given": "Gina-Isabelle"
}
],
"container-title-short": "Psychol Med",
"volume": "56",
"page": "e96",
"DOI": "10.1017/s0033291726104103",
"PMID": "41943954",
"PMCID": "PMC13079226",
"ISSN": "0033-2917",
"publisher": "Cambridge University Press",
"URL": "https://doi.org/10.1017/s0033291726104103",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
7
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41398-026-04140-0 [code]
Predicting stress response trajectories: Differential contributions of limbic and prefrontal regions to cortisol and affective responses.
Journal: Translational psychiatry
In common: OSF echja, 20 references, 9 authors
[2] doi:10.1038/s41514-026-00456-9 [code]
Exploring the link between body physiology and cognition: the role of the brain and aging.
Journal: npj aging
In common: Nilearn, NiBabel, statsmodels, 6 other tools, 3 references
[3] doi:10.21203/rs.3.rs-9914920/v1 [code]
Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group
Journal: Research Square (preprint)
In common: SHAP, FreeSurfer, Nilearn, 8 other tools
[4] doi:10.1038/s42003-026-09956-6 [code]
Linking changes in sulcal morphometry to cognitive development from childhood to adolescence.
Journal: Communications biology
In common: Nilearn, NiBabel, statsmodels, 6 other tools, developmental, 2 references
[5] doi:10.1038/s41467-026-71555-0 [code]
A deep representation learning model to predict response to vagus nerve stimulation.
Journal: Nature communications
In common: SHAP, FreeSurfer, NiBabel, 6 other tools, 2 references
[6] doi:10.1016/j.nicl.2026.104012 [code]
Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder.
Journal: NeuroImage. Clinical
In common: SHAP, FreeSurfer, NiBabel, 7 other tools, 1 reference
[7] doi:10.1162/imag.a.1362 [code]
Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: FreeSurfer, Nilearn, NiBabel, 6 other tools, 2 references
[8] doi:10.1007/s00234-026-04103-8 [code]
Enhanced detection of subtle cortical abnormalities in focal epilepsy using 7 T MRI surface-based models and graph neural networks.
Journal: Neuroradiology
In common: FreeSurfer, Nilearn, NiBabel, 7 other tools, 1 reference
[9] doi:10.7554/elife.107933 [code]
Modality-agnostic decoding of vision and language from fMRI.
Journal: eLife
In common: FreeSurfer, Nilearn, NiBabel, 7 other tools, 1 reference
[10] doi:10.1038/s41398-026-04157-5 [code]
Association of glymphatic function with 40-Hz neural oscillations, systemic metabolic markers, and cognitive performance in healthy aging adults: An EEG and MRI study.
Journal: Translational psychiatry
In common: FreeSurfer, Nilearn, NiBabel, 6 other tools, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.